144 research outputs found
Asymptotics of Discrete MDL for Online Prediction
Minimum Description Length (MDL) is an important principle for induction and
prediction, with strong relations to optimal Bayesian learning. This paper
deals with learning non-i.i.d. processes by means of two-part MDL, where the
underlying model class is countable. We consider the online learning framework,
i.e. observations come in one by one, and the predictor is allowed to update
his state of mind after each time step. We identify two ways of predicting by
MDL for this setup, namely a static} and a dynamic one. (A third variant,
hybrid MDL, will turn out inferior.) We will prove that under the only
assumption that the data is generated by a distribution contained in the model
class, the MDL predictions converge to the true values almost surely. This is
accomplished by proving finite bounds on the quadratic, the Hellinger, and the
Kullback-Leibler loss of the MDL learner, which are however exponentially worse
than for Bayesian prediction. We demonstrate that these bounds are sharp, even
for model classes containing only Bernoulli distributions. We show how these
bounds imply regret bounds for arbitrary loss functions. Our results apply to a
wide range of setups, namely sequence prediction, pattern classification,
regression, and universal induction in the sense of Algorithmic Information
Theory among others.Comment: 34 page
Strong Asymptotic Assertions for Discrete MDL in Regression and Classification
We study the properties of the MDL (or maximum penalized complexity)
estimator for Regression and Classification, where the underlying model class
is countable. We show in particular a finite bound on the Hellinger losses
under the only assumption that there is a "true" model contained in the class.
This implies almost sure convergence of the predictive distribution to the true
one at a fast rate. It corresponds to Solomonoff's central theorem of universal
induction, however with a bound that is exponentially larger.Comment: 6 two-column page
Discrete MDL Predicts in Total Variation
The Minimum Description Length (MDL) principle selects the model that has the
shortest code for data plus model. We show that for a countable class of
models, MDL predictions are close to the true distribution in a strong sense.
The result is completely general. No independence, ergodicity, stationarity,
identifiability, or other assumption on the model class need to be made. More
formally, we show that for any countable class of models, the distributions
selected by MDL (or MAP) asymptotically predict (merge with) the true measure
in the class in total variation distance. Implications for non-i.i.d. domains
like time-series forecasting, discriminative learning, and reinforcement
learning are discussed.Comment: 15 LaTeX page
On the Convergence Speed of MDL Predictions for Bernoulli Sequences
We consider the Minimum Description Length principle for online sequence
prediction. If the underlying model class is discrete, then the total expected
square loss is a particularly interesting performance measure: (a) this
quantity is bounded, implying convergence with probability one, and (b) it
additionally specifies a `rate of convergence'. Generally, for MDL only
exponential loss bounds hold, as opposed to the linear bounds for a Bayes
mixture. We show that this is even the case if the model class contains only
Bernoulli distributions. We derive a new upper bound on the prediction error
for countable Bernoulli classes. This implies a small bound (comparable to the
one for Bayes mixtures) for certain important model classes. The results apply
to many Machine Learning tasks including classification and hypothesis testing.
We provide arguments that our theorems generalize to countable classes of
i.i.d. models.Comment: 17 page
Offline to Online Conversion
We consider the problem of converting offline estimators into an online
predictor or estimator with small extra regret. Formally this is the problem of
merging a collection of probability measures over strings of length 1,2,3,...
into a single probability measure over infinite sequences. We describe various
approaches and their pros and cons on various examples. As a side-result we
give an elementary non-heuristic purely combinatoric derivation of Turing's
famous estimator. Our main technical contribution is to determine the
computational complexity of online estimators with good guarantees in general.Comment: 20 LaTeX page
Indefinitely Oscillating Martingales
We construct a class of nonnegative martingale processes that oscillate
indefinitely with high probability. For these processes, we state a uniform
rate of the number of oscillations and show that this rate is asymptotically
close to the theoretical upper bound. These bounds on probability and
expectation of the number of upcrossings are compared to classical bounds from
the martingale literature. We discuss two applications. First, our results
imply that the limit of the minimum description length operator may not exist.
Second, we give bounds on how often one can change one's belief in a given
hypothesis when observing a stream of data.Comment: ALT 2014, extended technical repor
MDL Convergence Speed for Bernoulli Sequences
The Minimum Description Length principle for online sequence
estimation/prediction in a proper learning setup is studied. If the underlying
model class is discrete, then the total expected square loss is a particularly
interesting performance measure: (a) this quantity is finitely bounded,
implying convergence with probability one, and (b) it additionally specifies
the convergence speed. For MDL, in general one can only have loss bounds which
are finite but exponentially larger than those for Bayes mixtures. We show that
this is even the case if the model class contains only Bernoulli distributions.
We derive a new upper bound on the prediction error for countable Bernoulli
classes. This implies a small bound (comparable to the one for Bayes mixtures)
for certain important model classes. We discuss the application to Machine
Learning tasks such as classification and hypothesis testing, and
generalization to countable classes of i.i.d. models.Comment: 28 page
On Universal Prediction and Bayesian Confirmation
The Bayesian framework is a well-studied and successful framework for
inductive reasoning, which includes hypothesis testing and confirmation,
parameter estimation, sequence prediction, classification, and regression. But
standard statistical guidelines for choosing the model class and prior are not
always available or fail, in particular in complex situations. Solomonoff
completed the Bayesian framework by providing a rigorous, unique, formal, and
universal choice for the model class and the prior. We discuss in breadth how
and in which sense universal (non-i.i.d.) sequence prediction solves various
(philosophical) problems of traditional Bayesian sequence prediction. We show
that Solomonoff's model possesses many desirable properties: Strong total and
weak instantaneous bounds, and in contrast to most classical continuous prior
densities has no zero p(oste)rior problem, i.e. can confirm universal
hypotheses, is reparametrization and regrouping invariant, and avoids the
old-evidence and updating problem. It even performs well (actually better) in
non-computable environments.Comment: 24 page
Indefinitely oscillating martingales
We construct a class of nonnegative martingale processes that oscillate indefinitely with high probability. For these processes, we state a uniform rate of the number of oscillations for a given magnitude and show that this rate is asymptotically close to the theoretical upper bound. These bounds on probability and expectation of the number of upcrossings are compared to classical bounds from the martingale literature. We discuss two applications. First, our results imply that the limit of the minimum description length operator may not exist. Second, we give bounds on how often one can change one’s belief in a given hypothesis when observing a stream of data
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